3,190 research outputs found
Biometric presentation attack detection: beyond the visible spectrum
The increased need for unattended authentication in
multiple scenarios has motivated a wide deployment of biometric
systems in the last few years. This has in turn led to the
disclosure of security concerns specifically related to biometric
systems. Among them, presentation attacks (PAs, i.e., attempts
to log into the system with a fake biometric characteristic or
presentation attack instrument) pose a severe threat to the
security of the system: any person could eventually fabricate
or order a gummy finger or face mask to impersonate someone
else. In this context, we present a novel fingerprint presentation
attack detection (PAD) scheme based on i) a new capture device
able to acquire images within the short wave infrared (SWIR)
spectrum, and i i) an in-depth analysis of several state-of-theart
techniques based on both handcrafted and deep learning
features. The approach is evaluated on a database comprising
over 4700 samples, stemming from 562 different subjects and
35 different presentation attack instrument (PAI) species. The
results show the soundness of the proposed approach with a
detection equal error rate (D-EER) as low as 1.35% even in a
realistic scenario where five different PAI species are considered
only for testing purposes (i.e., unknown attacks
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Automatically discovering composable abstractions from raw perceptual data is
a long-standing challenge in machine learning. Recent slot-based neural
networks that learn about objects in a self-supervised manner have made
exciting progress in this direction. However, they typically fall short at
adequately capturing spatial symmetries present in the visual world, which
leads to sample inefficiency, such as when entangling object appearance and
pose. In this paper, we present a simple yet highly effective method for
incorporating spatial symmetries via slot-centric reference frames. We
incorporate equivariance to per-object pose transformations into the attention
and generation mechanism of Slot Attention by translating, scaling, and
rotating position encodings. These changes result in little computational
overhead, are easy to implement, and can result in large gains in terms of data
efficiency and overall improvements to object discovery. We evaluate our method
on a wide range of synthetic object discovery benchmarks namely CLEVR,
Tetrominoes, CLEVRTex, Objects Room and MultiShapeNet, and show promising
improvements on the challenging real-world Waymo Open dataset.Comment: Accepted at ICML 2023. Project page: https://invariantsa.github.io
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